1. Introduction
In recent years, more and more medical device applications have entered the healthcare landscape, therefore placing decision-making power directly into patients’ hands. Especially in chronic diseases like multiple sclerosis (MS), the continuous collection of patient-generated data is crucial to understanding the very individual path of a patient’s disease and treatment journey.
Currently, it is not uncommon for people diagnosed with MS to see their doctor only two to three times a year [
1]. These long periods between medical check-ups can be perceived as a gap in patient care. As the number of MS cases increases worldwide [
2], digital companion apps may be an option for many patients [
3]. Due to the individualistic nature of MS, the collection of real-time data on a longitudinal basis—along with a variety of digital biomarkers—afforded by such apps is becoming increasingly crucial to our understanding of this disease and its pathophysiology and progression [
4,
5]. Brisa is an example of an MS companion that allows the daily tracking of the range of symptoms MS patients suffer from. Monitoring symptoms could be crucial for detecting relapses and identifying progression, but it also empowers the patients to be part of the therapy decision-making process and exchange their journey in a community. Also, Ziemssen et al. have already shown the importance of a physician-completed tool for supporting physician–patient interaction in assessing signs of disease progression and uncovered the need for supporting tools [
6].
The collection of patient-centered data in chronic diseases such as MS is becoming increasingly important. Understanding how patients experience their symptoms and how these symptoms affect their daily lives is essential to improve patient care and treatment outcomes [
7]. Additionally, the possibility of close symptom monitoring by patients themselves could make a crucial contribution to knowledge about and identification of signs of disease progression, as well as empowering patients to steer towards a personalized therapy approach.
The optimal disease management for MS is being revised with an increasing emphasis on personalized treatment approaches [
8]. Over the past 25 years, the treatment approach and disease management have changed significantly [
9]. While patients have traditionally been treated in an escalating manner starting from lower efficacy treatment and moving to higher efficacy treatment only after ongoing treatment fails [
10], increasing evidence emphasizes the importance of an early intervention following diagnosis accompanied by optimization of treatment for each patient individually [
11,
12]. Variations in symptoms can occur from person to person depending on the severity of the neuronal damage, and although high-efficacy therapies are available, some patients still suffer from relapses and subclinical disease activity. Relapses and disease progression can take place in different functional systems through a wide range of symptoms that include fatigue, impaired motor function, spasticity, pain, gait disturbance, speech problems and cognitive impairment [
4,
13,
14]. These symptoms can have a negative impact on individuals both physically and psychologically, with the progression of this disease leading to difficulties in performing everyday tasks due to impaired motor skills and affecting social life and the ability to live independently [
15]. Especially in a disease termed ‘disease of a thousand faces’, it is crucial to gain an understanding of all relevant symptoms not only to assess signs of disease activity and progression, but also to improve patients’ disease management.
While progress has been made in terms of developing new treatments due to a more comprehensive understanding of the course and pathogenesis of MS [
14], MS is still considered to be an incurable disease [
4]. A variety of medications, with moderate to high efficacy, are available for the treatment of MS, particularly the relapsing–remitting form (RRMS) [
16]. For the primary progressive form of MS (PPMS), ocrelizumab is currently the only approved medication [
9,
10]. Therefore, ocrelizumab, approved for both RMS and PPMS, occupies a special position in the treatment landscape and has already been proven to fulfill the safety profile, treatment persistence and adherence of clinical trials in a real-world MS population [
17]. The current study focused on characteristics, symptoms and PRO reporting and the corresponding scores of Brisa users who are treated with medium- and high-efficacy drugs, with an individual assessment of ocrelizumab users representing the biggest treatment cohort of Brisa users.
The aim of this work is to better understand the Brisa users treated with high- and medium-efficacy therapies and to assess differences reflected in symptom and PRO reporting and scores.
2. Methods
2.1. Ethical Approval
Since the data were aggregated and analyzed retrospectively and Brisa users consented to the use of their data, ethical approval is not applicable.
2.2. Data Source
Brisa (version 2.1.0) is a smartphone application available for Android and iOS devices intended to support MS patients in their day-to-day lives by offering guidance and advice in a variety of areas. The data source and collection were described previously [
18,
19]
2.3. Inclusion Criteria of Study Cohort
This retrospective, descriptive analysis consists of data from Brisa users who registered between 6 August 2021 and 8 September 2022. Of all registered Brisa users (n = 6092), 69.4% (n = 4228) gave consent to the use of their data for scientific purposes. In the timeframe of analysis, on average, 65 users actively used the app (answered a PRO and/or reported a symptom) on a daily basis, 164 weekly and 412 monthly. Of the unique users, 37.7% (n = 1593) fulfilled the inclusion criteria listed below and were therefore included in our analysis. The inclusion criteria were age 18–80 years, gender reported, MS type reported, year of diagnosis reported, medication reported, consent for health data usage for scientific purposes, answered at least one questionnaire completely and reported at least one symptom.
2.4. Data Processing and Analysis
Data processing and analysis were performed using Python (version 3.9). To study the demographic characteristics of Brisa users/patients such as gender, age, type of MS, time since MS diagnosis and medications, onboarding information was examined. For each parameter analyzed, only those who responded and provided details for the corresponding parameter were considered. Users with skipped (categorized as ‘unknown’) or invalid entries were excluded. Additional inclusion criteria specific for each parameter under investigation and their classification were as follows:
Age was calculated using the year of birth. For all age-related analyses, users between the ages of 18 and 85 were considered. They were further classified into 5 subgroups based on age: 18–25, 26–35, 36–45, 46–55, >55 years.
To study gender-based age distribution, users who reported both parameters were included. This applies to all cases throughout the analysis where two or more parameters were involved, unless mentioned otherwise.
Time since diagnosis was computed using the year of diagnosis. All entries up to 30 years since diagnosis were considered for analysis. Based on the years since diagnosis, users were further grouped into 5 categories: 0–1 year, 2–5 years, 6–10 years, 11–20 years, and 21–30 years.
All patients with known medication entries were included.
Based on recent publications [
20], users were grouped into moderate-efficacy treatment users (METUs), high-efficacy treatment users (HETUs) and ocrelizumab users (OUs) (
Table 1)
To identify the symptoms that predominantly affected our study cohort, users who tracked at least 1 symptom once after onboarding were considered.
2.5. Statistical Methodology
Statistical analysis was performed using Graphpad (version 10.0). To calculate significant differences in base scores, we ran a multiple-comparison Kruskal–Wallis test correcting for p-values using Dunn’s test. p-values < 0.05 were considered significant.
4. Discussion
While clinical trial data have delivered an indispensable understanding of the efficiency and outcomes of disease-modifying treatments, data regarding day-to-day disease management based on RWE are still lacking. Although there are many MS companion apps on the market focusing on various use cases like medication tracking, symptom tracking or lifestyle tracking, Brisa is a first-of-its-kind medical device smartphone application in Germany intended not only to support MS patients in their day-to-day lives, but also to collect patient-centered data and experiences.
Our retrospective analysis focused on the well-being of Brisa users on medium- and high-efficacy drugs, with a focus on ocrelizumab patients, and aimed to generate insights to narrow the gap between experimental research, clinical studies and real-world data of MS patients.
Twenty-five percent of Brisa users declared the usage of ocrelizumab as their treatment. The characteristics of the ocrelizumab Brisa cohort are comparable to the findings of previous clinical trials [
21,
22] and non-interventional studies such as the CONFIDENCE study with more than 3000 participants [
17]. With a mean age of 41.9 ± 11.2 years, our cohort was around 10 years younger than the participants of the CONFIDENCE study (51.3 ± 10.0 years) [
23] and 5.6 years younger than the average age reported by the MS registry (47.5 ± 12.5 years) [
24]. Recent studies show that younger users have a significantly higher affinity for using mobile technology in disease management [
25] when compared to older users, which could explain why the average age of users was lower than in the registry and study data. The mean time since diagnosis in our cohort was 9.1 ± 7.2 years, whereas the mean time since diagnosis of the CONFIDENCE study participants was 5.5 ± 6.7 years. Our cohort was representative of the gender distribution (75% female) which is also reported by the MS registry in Germany, where 70.9% of MS patients are female. In our cohort, the share of RRMS/SPMS users decreased with age, whereas PPMS increased with age. This inverse relationship is also reflected in current data, where the median age of patients diagnosed with PPMS was 50 years, whereas RRMS patients are usually diagnosed in their 20s to 30s [
26]. Overall, our cohort is representative of the described MS population in Germany, which allows us to extrapolate our findings to the overall MS population in Germany.
Our ocrelizumab cohort consisted of 73% users diagnosed with RRMS/SPMS and 27% diagnosed with PPMS. Symptoms of concern depend on MS type, which becomes apparent when analyzing the top five reported symptoms in the ocrelizumab cohort by MS type. According to the National MS Society, patients diagnosed with PPMS tend to have more lesions in the spinal cord than in the brain and therefore tend to experience more problems walking [
27]. PPMS-diagnosed users are concerned with symptoms that affect the disability aspect, like spasticity cramps and leg foot lifting disorder, whereas RRMS/SPMS-diagnosed users are more concerned about the sensory spectrum of symptoms like tingling and pain. As ocrelizumab is the only highly effective treatment approved for PPMS, no PPMS-specific symptoms are reported in the top five in the HETU or METU group.
PDDS was only in the top five answered PROs in the OU and HETU groups, but not the METU cohort. Our patients in the OU and HETU groups were older than those in the METU group, which could be one explanation as to why only these two groups answered PDDS questionnaires under the most common PROs. Overall, we would have expected PDDS to be under the most tracked PROs since it is an established way to determine the patient’s disease progression regarding disability. The voluntary aspect of answering the chosen PRO may be the reason other PROs were answered more frequently.
Within the OU cohort, the average disability (PDDS) score in our PPMS cohort using ocrelizumab was 3.8, which is slightly lower than the score reported in the study on the real-world safety and effectiveness of ocrelizumab in patients with PPMS [
23] which showed that the majority of patients had a significant disability at baseline (EDSS ≥ 4.0). The inclusion of PPMS patients, who experience more effects on mobility during their disease course, may be one additional explanation for the higher disability (PDDS) scores with increasing age in the OU cohort.
In the OU cohort, both the disability (PDDS) and pain (PES) scores increased also with increasing age. Similarly, the symptom ‘leg foot lifting disorder’ concerns mainly the older users in the ocrelizumab cohort. This is most likely linked to a potential progression independent of relapses (PIRA), where the severity of disability increases over time [
28].
Fatigue is one of the most common symptoms of MS, affecting about 80% of people [
29]. In our cohort, across all treatment groups, the quick-check symptom fatigue is also tracked by the largest proportion of users. Interestingly, when comparing to the most reported PROs in the respective treatment cohort, MFIS-5 (fatigue) is only completed by around 22% of METUs, whereas fatigue PRO (MFIS-5) is not in the top five completed PROs in the OU and HETU treatment groups. This could be due to several reasons. Symptom tracking in the Brisa app occurs daily, whereas the PROs are completed every 2 weeks. It is possible that since fatigue is such a common symptom and affects most MS patients, users do not see the need to track the PROs in addition to the daily symptom quick check. Instead, users focus on the disease symptoms that are underrepresented in the daily symptom check.
Although depression in its various forms is one of the most common symptoms of MS [
29], it is striking that young users aged 18–25 years tracked this symptom more often than older age groups. One explanation could be that younger people are more aware of psychological issues, but more research is needed to understand the reasons behind this.
A follow-up analysis comparing the scores over time will give greater insight into the ability to track disease development using a companion app. We could not identify any significant differences in baseline PRO scores for disability (PDQ-5), bowel control (BWCS), pain (PES) and vision (IVIS5) between the three treatment cohorts.
Overall, both the daily symptom quick check and the PROs are completed more often in the OU treatment group compared to the other treatment groups. One hypothesis is that the patients on a high-efficacy treatment are perceiving a measurable improvement and want to document their therapy success, although this hypothesis would need to be verified in a follow-up study to measure the perceived outcomes of patient treatments.
Analyses involving patient-reported data entail the probability of a small percentage of false data inputs by users themselves, which cannot be overlooked. Also, the lack of additional information, especially information regarding the frequency of flare-ups, periods of remission, other types of medications used, etc., limited us from gathering further insights and drawing associations between symptoms and medications. With the continuation of the application development, these features could potentially contribute to a fuller picture of patient-reported data.